Sunil Padiyar is the Chief Technology Officer (CTO) of Trintech, a global leader in AI Financial Close solutions.
getty
Human-in-the-loop AI has quickly become part of the standard conversation in finance. As organizations introduce more automation into the financial close, keeping people involved in the process feels both practical and necessary. Finance teams operate in an environment where accuracy, accountability and auditability matter every day.
But human oversight by itself does not create trust.
Finance leaders care about speed, but speed alone is not enough. They need to understand where results came from, why decisions were made and whether those decisions will hold up under scrutiny. That requires more than placing a reviewer at the end of an automated workflow. Confidence comes from the design of the system itself.
Many early AI deployments have treated human-in-the-loop as a safeguard. AI generates a recommendation, and someone reviews and approves it before action is taken. While that reduces risk, it does not fully address the trust problem.
In practice, manual review often creates bottlenecks and inconsistency, especially for teams already stretched during the close. Over time, it can also shift the burden of validation back onto finance professionals instead of improving the process itself.
More importantly, oversight alone does not answer one of the most important questions in finance:
Without supporting evidence, traceability and a clear explanation of the underlying logic, even an accurate result can create concerns during audit and review. Finance teams are expected not only to deliver outcomes but also to demonstrate how those outcomes were reached.
In finance, speed without explainability is simply unmanaged risk.
Moving from oversight to confidence requires AI to operate inside a broader system of financial control.
That starts with trusted data. In many cases, the challenge is not the AI model itself. It is the lack of consistent, reconciled financial data across systems. AI can only produce reliable outputs when the underlying data is governed and aligned. Otherwise, automation simply accelerates existing problems.
AI also works best when it is part of the close process itself, not layered on top of it. Reconciliations, journal entries and certifications already operate within established approval structures and policies. Many finance teams already have strong controls in place. The challenge is making sure AI operates within those same structures rather than around them.
Just as important is visibility into how recommendations are made. Finance teams, controllers and auditors need to follow the logic behind AI-generated outputs. For example, if AI recommends clearing a reconciliation exception, teams should be able to see the transaction history, matching logic and supporting rationale behind that recommendation.
Human oversight still matters, especially for higher-risk decisions. But oversight alone is not a control framework.
For organizations adopting AI during the financial close, success depends less on adding another tool and more on strengthening the operational foundation underneath it. Most finance teams do not need disconnected AI capabilities. They need cleaner data, tighter workflows and better visibility into how decisions are being made throughout the close process.
The first step is improving data consistency and governance across ERP and adjacent systems. We consistently see AI perform better when organizations have already invested in structured and reliable financial data.
From there, organizations can introduce AI directly into existing closed workflows while keeping approvals, controls and audit trails intact. That approach allows automation to support governance rather than bypass it.
One of the mistakes organizations make early on is applying the same level of review to every AI-generated output. Finance teams should spend their time on judgment-heavy exceptions, not repetitive low-risk tasks that can be consistently automated. In most finance organizations, adoption happens gradually as teams build confidence through repeatable, explainable outcomes. Trust develops over time when teams can see measurable improvements in accuracy, efficiency and cycle time.
When these elements come together, finance teams operate differently.
Issues are identified earlier in the process. Teams spend less time validating routine work and more time focused on analysis and decision-making. Audit readiness becomes part of day-to-day operations instead of a separate exercise at the end of the cycle.
This is where AI delivers its greatest value in finance. Not by replacing human judgment, but by supporting it with systems that are transparent, consistent and aligned with the control expectations finance organizations already operate under.
Human-in-the-loop AI is an important step forward, but it is not the end state. The organizations that will lead in this next phase will be the ones that build AI into finance-grade systems of control, where data, workflows and intelligence work together in a way that produces outcomes teams can trust and defend.
Because in finance, trust is not created at the point of review.
It is built into the system itself.
Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?
此内容由惯性聚合(RSS阅读器)自动聚合整理，仅供阅读参考。 原文来自 — 版权归原作者所有。